Information Extraction from Documents is crucial for improving the efficiency of form processing and reducing human labor. Information extraction is often formulated as a field extraction task. Earlier methods [1]}, [2]} extract information from document with the help of registered templates in the system. Later, [3]} propose an invoice field extractor by using an Attend, Copy, Parse architecture.  [4]} present a field-value pairing framework that learns the representations of fields and value candidates in the same feature space using metric learning. [5]} propose a span extraction approach to extract the start and end of a value for each queried field. [6]} introduce a field extraction system that can be trained with large-scale unlabeled documents. [7]} propose 14 form transformations to mimic the variations of forms for robustness evaluation of transformer-based field extractors. Unlike previous methods that aim to extracting values for a pre-defined set of fields, our method targets at retrieving values for arbitrary queries.
